MikhailTal
MikhailTal on Hacker News
It is but its average, not median. Plenty of super senior people that Anthropic poached for clout, network and for them not to work anyone else. Plus a lot of the early researches. A lot of cash going there
> by easing off on the R&D spend
you could not be more wrong, you cannot IPO if you say product is good enough. The whole point, the bet why its worth trillions is because they will reach AGI and "singularity" first, so the gap vs open source will only increase. If you say product is great as is, then you will be caught up and game over
.. HN is actually hilarious
We are barely 4 years in since the release of a historically important tech. Of course the next few years are incremental. thats how it always works.
Even last year, before opus 4.5, long-horizon agents didnt really work. I would not say the last 12 months are only incremental
any company can roll instagram(just simple image viewer) or whatsapp(just messaging) out though with the same logic. Does not mean you can compete
.. Don't you think it suspicious/sounds too good to be true?
A newly created product space with insane margins, and easy to compete in?
Easy to compete+huge margins -> lots of competitors who compete on price -> commotidization -> lower margins -> not good
Sandboxes look a bit commoditized. You cannot 10x on basically what's just a VM with a few agent-friendly features in 2026. Hence competing on purely price as a new startup is a bad idea imo. As you mentioned hyperscalers have insane margins, but if this turns out to be a real market, they will lower cost, even just eat the loss if they want and take you out of business. Without any hardware innovation, it's tough for me to see this working. If you are going with a customization angle, again, no moat and plenty of competitors
6.1 is Astra minor. Way more capable but also way heavier+slower. Its really 2 different models, they just shipped it as sol to recover from the gpt6 disaster lunch, where they tried to pass terra 6(or a cheaper model) as sol but it was worse than expected
No. If your engineers can produce more ROI working on another product, it does not matter that now they are 10x more efficient. They will also be 10x in the other, more worth it product
> This is a classic test request
Isn't this basically the model admitting it was trained on this? Otherwise why would it think a pelican svg is a usual request?
No? from a quick skim it doesnt look like it goes into the system prompt everytime, you just use search/grep over it. Pretty much most memory approaches relying on a big set of info where agent chooses what to 'recall'. Its like any other tool
This is very bad logic
1) Humans also are trained on a subset of human knowledge.
2)A lot of papers are just about experimenting something, and then applying simple stats. Eg empirical studies, around 1/3rd of published papers. Like, we tried this drug or did this experiment, from a sample size X here are the results. An expert is needed to maybe comment on the conclusion/hypothesis of the underlying suspected mechanism, but LLMs are still very useful on catching bad statistics or p hacking (so so common)
This is technically true, but when people talk about randomness, its not only about same input-> different output, like temperature>0 and the things you said.
Its also about very similar inputs -> different outputs. Even with everything you said, yes, same input would result consistently into same output, but sliightly different input and you might get completely different/semantic answer.
Aware that this is essentially a soon to be deprecated service. As any vendor you need to know the life expectancy of it, plus forecast/predict any pricing changes. You really do not want to become dependent. Even if competitors exist, there still some non trivial cost to switch
Again, this is HN bubble stuff. The vast majority of people do not mind oauth or account.
> if you hook up to the fediverse
No this is the hard problem. HN people live in a bubble. Anything else than signup with oauth and then having access to everyone and its too hard and 99% of people will click away.
As usual, the real tldr is in the privacy policy
https://generalresearch.com/supplier/privacy-policy/notice-t...> General Research Laboratories, LLC (“GRL”) is an aggregator of market research surveys in multiple marketplaces for business customers. GRL does not typically host consumer surveys which are conducted by other consumer-facing organizations....
So, just a middleman to give you fake/shady at best survey responses to pad your numbers, so big enterprises/concultancies can have data that say whatever they want. The rest of the website is just BS
Very fascinating, super interesting engineering. Although i do find it very funny how they just bypass a massive vulnerability, basically zero data isolation (even between good actors, let alone bad ones) with 3 sentences. Only in the llm space you can slap a massive limitation like this in the middle of the article and continue like nothing happened
> Whatever anyone tells Audel becomes part of the single experience that every other conversation draws on. In practice, Audel is bad at keeping secrets. Ask it what it’s been working on with someone else and it will often just tell you, even though we’ve asked it not to. We also haven’t studied what happens when two people give conflicting instructions. For now, we assume anything you tell Audel is shared with everyone on the team.
Not all agents have access to a sandbox/cli/code execution environment to run arbitrary api calls etc. MCP helps by essentially having another tool call without needing a sandbox. If you do have a sandbox, then might as well do codemode if you insist on mcp
https://blog.cloudflare.com/code-mode/
This is terrible. Models have been RLed on looking at the previous tool call chain, and reasoning. No chance this does not reduce performance. The point of compaction is that it also includes useful signal from the tool outputs itself so agent does not repeat it afterwards
When a company gives away service a heavily subsidized service as a promo, the full cost of serving it (compute) can get classified as sales and marketing instead of just cost of revenue, which makes your gross margin look better!
> Google DeepMind: We are building strong momentum: Flash is in high demand, our Cyber model is live, and Gemma models have surpassed 900M+ downloads
Considering these are the best stats they could find, gemini usage+general situation must be really, really bleak.
High demand means nothing. A model being live is nothing to brag about. And gemma downloads also can be from auto CI pipelines etc. Nothing concrete
It's a smart approach, definitely interesting. It all hinges on quality of course which im not convinced.
τ³-Banking is the only one which you show better accuracy and cheaper. If i'm reading the blog results right, for deepswe and terminalbench, you are worse+cheaper than frontier, and better+more expensive than just small models. Which is exactly what i would expect even for a router that switches at random.
Speaking of random routing, this would be a great ablation study as well. What about also if you route each request to a tiny 7B model classifier? Why is your approach SOTA?
this is /goal in claude code/codex. also basically a slightly improved ralph loop
is this really that surprising?
Exploitgym prompts are tuned for a model to do everything it can to achieve a cybersec/exploit task. And we know that models are good at finding vulverabiltiies.
Its just random that the sandbox itself was buggy. But all that happened here is that we told a model "do everything you can to achieve your goal of hacking X" And it just hacked Y as a roundabout way of hacking X.
Imo its PR for OpenAI to also start the mythos class mysterious unreleased model hype.
From HF statement: "AI safety won't be solved by any single company working in secret". So now we have TWO companies working in secret
the OP asked why, you more described the how.
What is the benefit of all this?
"Lottery winner says you should not gamble"
Also, the OP just does not understand how the market works anyway. Surely if it was obvious that investing in fresh IPOs is a bad move, all of the big boys (banks, hedge funds etc) would short them to the point of equalising anyway. Maybe not to the absolute efficient point, but still, why do people think they can see such a huge obvious trend, and also assume that other people cannot see it?
This is not a new situation. This was happening also when good vision models like alexa net were coming through, especially for OCR. Companies had choice between cloud or self hosting with GPUs. But turns out, problem is usage patterns.
Your usage will peak during certain timezone work hours(even if you are a huge multinational company most of your engineers/users tend to be from only a few locations), so then you have a bunch of gpus doing nothing the rest of the day.
especially with latency sensitive stuff, this is a decades old tradeoff problem, its not unique to llms